The FSC task consists of two phases: (1) Pre-training. Researchers employ the base data \(\mathcal {D}_{b}\)  to pre-train a CNN-based model, which consists of feature extractor \(\mathcal {J}(\cdot )\)  and base classifier \(\mathcal {M}_{b}(\cdot )\) .
(2) Meta-testing.
We freeze the pre-trained feature extractor and utilise it to extract the feature of novel data \(\mathcal {D}_{n} = \lbrace \mathcal {S}, \mathcal {U}, \mathcal {Q}\rbrace \) , where \(\mathcal {S}\) , \(\mathcal {U}\)  and \(\mathcal {Q}\)  denote support set (labeled training samples), unlabeled set (unlabeled training samples) and query set (to-be-classified testing samples).
\(\mathcal {S} {\hspace{2.0pt}} \cap {\hspace{2.0pt}} \mathcal {U} = \emptyset \) ,
\(\mathcal {S} {\hspace{2.0pt}} \cap {\hspace{2.0pt}} \mathcal {Q} = \emptyset \) ,
\(\mathcal {Q} {\hspace{2.0pt}} \cap {\hspace{2.0pt}} \mathcal {U} = \emptyset \) .
According to whether using the unlabeled data when constructing the novel classifier \(\mathcal {M}_{n}(\cdot )\)  to recognize categories of query data, we split the FSL into two settings : supervised setting and semi-supervised setting. Section REF  show more details.
In classification stage, we follow the standard paradigm with \(C\) -\(way\) -\(K\) -\(shot\)  per episode as [1]}, where \(C\) -\(way\)  denotes \(C\)  classes, and \(K\) -\(shot\)  indicates \(K\)  samples per class. The reported results are the average of accuracies of all the episodes with \(95\%\)  confidence intervals.
